Argument mining is natural language processing technology aimed at identifying arguments in text. Furthermore, the approach is being developed to identify the premises and claims of those arguments, and to identify the relationships between arguments including support and attack relationships. In this paper, we assume that an argument map contains the premises and claims of arguments, and support and attack relationships between them, that have been identified by argument mining. So from a piece of text, we assume an argument map is obtained automatically by natural language processing. However, to understand and to automatically analyse that argument map, it would be desirable to instantiate that argument map with logical arguments. Once we have the logical representation of the arguments in an argument map, we can use automated reasoning to analyze the argumentation (e.g. check consistency of premises, check validity of claims, and check the labelling on each arc corresponds with thw logical arguments). We address this need by using classical logic for representing the explicit information in the text, and using default logic for representing the implicit information in the text. In order to investigate our proposal, we consider some specific options for instantiation.

翻译：论证挖掘是一种旨在识别文本中论证的自然语言处理技术。此外，该方法正被发展为识别这些论证的前提与主张，并识别论证之间的关系（包括支持与攻击关系）。在本文中，我们假设论证图包含由论证挖掘识别出的论证前提与主张，以及它们之间的支持与攻击关系。因此，我们假设通过自然语言处理可从一段文本中自动获得论证图。然而，为了理解并自动分析该论证图，需要利用逻辑论证对该论证图进行实例化。一旦获得论证图中论证的逻辑表示，我们便可使用自动推理来分析论证过程（例如检查前提的一致性、验证主张的有效性，并检验每条弧上的标注是否与逻辑论证相符）。我们通过使用经典逻辑表示文本中的显式信息，并使用默认逻辑表示文本中的隐式信息，来满足这一需求。为探究我们的提议，我们考虑了一些具体的实例化方案。